River Segmentation of Remote Sensing Images Based on Composite Attention Network
River segmentation of remote sensing images is of important research significance and application value for environmental monitoring, disaster warning, and agricultural planning in an area. In this study, we propose a river segmentation model in remote sensing images based on composite attention net...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/7750281 |
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| _version_ | 1832561331024166912 |
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| author | Zhiyong Fan Jianmin Hou Qiang Zang Yunjie Chen Fei Yan |
| author_facet | Zhiyong Fan Jianmin Hou Qiang Zang Yunjie Chen Fei Yan |
| author_sort | Zhiyong Fan |
| collection | DOAJ |
| description | River segmentation of remote sensing images is of important research significance and application value for environmental monitoring, disaster warning, and agricultural planning in an area. In this study, we propose a river segmentation model in remote sensing images based on composite attention network to solve the problems of abundant river details in images and the interference of non-river information including bridges, shadows, and roads. To improve the segmentation efficiency, a composite attention mechanism is firstly introduced in the central region of the network to obtain the global feature dependence of river information. Next, in this study, we dynamically combine binary cross-entropy loss that is designed for pixel-wise segmentation and the Dice coefficient loss that measures the similarity of two segmentation objects into a weighted one to optimize the training process of the proposed segmentation network. The experimental results show that compared with other semantic segmentation networks, the evaluation indexes of the proposed method are higher than those of others, and the river segmentation effect of CoANet model is significantly improved. This method can segment rivers in remote sensing images more accurately and coherently, which can meet the needs of subsequent research. |
| format | Article |
| id | doaj-art-fda1e970a53e48eb9f489b23ae0cc8f9 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-fda1e970a53e48eb9f489b23ae0cc8f92025-02-03T01:25:20ZengWileyComplexity1099-05262022-01-01202210.1155/2022/7750281River Segmentation of Remote Sensing Images Based on Composite Attention NetworkZhiyong Fan0Jianmin Hou1Qiang Zang2Yunjie Chen3Fei Yan4Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyCollaborative Innovation Center on Atmospheric Environment and Equipment TechnologyCollaborative Innovation Center on Atmospheric Environment and Equipment TechnologySchool of Math and StatisticsCollaborative Innovation Center on Atmospheric Environment and Equipment TechnologyRiver segmentation of remote sensing images is of important research significance and application value for environmental monitoring, disaster warning, and agricultural planning in an area. In this study, we propose a river segmentation model in remote sensing images based on composite attention network to solve the problems of abundant river details in images and the interference of non-river information including bridges, shadows, and roads. To improve the segmentation efficiency, a composite attention mechanism is firstly introduced in the central region of the network to obtain the global feature dependence of river information. Next, in this study, we dynamically combine binary cross-entropy loss that is designed for pixel-wise segmentation and the Dice coefficient loss that measures the similarity of two segmentation objects into a weighted one to optimize the training process of the proposed segmentation network. The experimental results show that compared with other semantic segmentation networks, the evaluation indexes of the proposed method are higher than those of others, and the river segmentation effect of CoANet model is significantly improved. This method can segment rivers in remote sensing images more accurately and coherently, which can meet the needs of subsequent research.http://dx.doi.org/10.1155/2022/7750281 |
| spellingShingle | Zhiyong Fan Jianmin Hou Qiang Zang Yunjie Chen Fei Yan River Segmentation of Remote Sensing Images Based on Composite Attention Network Complexity |
| title | River Segmentation of Remote Sensing Images Based on Composite Attention Network |
| title_full | River Segmentation of Remote Sensing Images Based on Composite Attention Network |
| title_fullStr | River Segmentation of Remote Sensing Images Based on Composite Attention Network |
| title_full_unstemmed | River Segmentation of Remote Sensing Images Based on Composite Attention Network |
| title_short | River Segmentation of Remote Sensing Images Based on Composite Attention Network |
| title_sort | river segmentation of remote sensing images based on composite attention network |
| url | http://dx.doi.org/10.1155/2022/7750281 |
| work_keys_str_mv | AT zhiyongfan riversegmentationofremotesensingimagesbasedoncompositeattentionnetwork AT jianminhou riversegmentationofremotesensingimagesbasedoncompositeattentionnetwork AT qiangzang riversegmentationofremotesensingimagesbasedoncompositeattentionnetwork AT yunjiechen riversegmentationofremotesensingimagesbasedoncompositeattentionnetwork AT feiyan riversegmentationofremotesensingimagesbasedoncompositeattentionnetwork |